Multi-Entity Dependence Learning With Rich Context via Conditional Variational Auto-Encoder
Authors: Luming Tang, Yexiang Xue, Di Chen, Carla Gomes
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on two computational sustainability related datasets. The first one is a crowd-sourced bird observation dataset collected from the e Bird citizen science project (Munson et al. 2012). The experiment results on e Bird and Amazon dataset are shown in Table 2 and 3, respectively. |
| Researcher Affiliation | Academia | Luming Tang Tsinghua University, China tlm14@mails.tsinghua.edu.cn Yexiang Xue Cornell University, USA yexiang@cs.cornell.edu Di Chen Cornell University, USA dc874@cornell.edu Carla P. Gomes Cornell University, USA gomes@cs.cornell.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or direct links to a code repository. |
| Open Datasets | Yes | The first one is a crowd-sourced bird observation dataset collected from the e Bird citizen science project (Munson et al. 2012). Crossed with the National Land Cover Dataset (NLCD) (Homer et al. 2015), we get a 15-dimension feature vector for each location. Our second application is the Amazon rainforest landscape analysis2, in which we tag satellite images with a few landscape categories. Raw satellite images were derived from Planet s full-frame analytic scene products using 4-band satellites in the sunsynchronous orbit and the International Space Station orbit. The organizers at Kaggle used Planet s visual product processor to transform raw images to 3-band jpg format. Each satellite image sample in this dataset contains an image chip covering a ground area of 0.9 km2. The chips were analyzed using the Crowd Flower3 platform to obtain ground-truth landscape composition. |
| Dataset Splits | Yes | We randomly choose 34,431 samples for training, validation and testing. The details of the two datasets are listed in table 1. |
| Hardware Specification | Yes | All the training and testing process of our proposed MEDL CVAE are performed on one NVIDIA Quadro 4000 GPU with 8GB memory. |
| Software Dependencies | No | The paper mentions "Adam optimizer (Kingma and Ba 2014) with learning rate of 10 4 and utilizing batch normalization (Ioffe and Szegedy 2015)" but does not provide specific version numbers for software libraries or dependencies (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | The whole training process lasts 300 epochs, using batch size of 512, Adam optimizer (Kingma and Ba 2014) with learning rate of 10 4 and utilizing batch normalization (Ioffe and Szegedy 2015), 0.8 dropout rate (Srivastava et al. 2014) and early stopping to accelerate the training process and to prevent overfitting. |